Diabetes and Hypertension in ELSA, OCTO, and RUSH
Counts by Wave
Counts the number of datapoints in the criterion availibe at each time interval.
by Wave : ELSA
by Wave : OCTO
by Wave : RUSHc
by Wave : RUSHe
by Wave : RUSHl
Counts by Age
Counts the number of datapoints in the criterion availibe at each time interval. ## by Age : ELSA 
by Age : OCTO
by Age : RUSHc
by Age : RUSHe
by Age : RUSHl
Observed Trajectories (Time)
Transparent blue lines represent observed trajectories. A random sample was selected from the study’s data to avoid overplotting. Solid red line represents the fixed effects of the intercept and slope that are quantified in the equation in the top right corner.
by Time : ELSA
by Time : OCTO
by Time : RUSHc
by Time : RUSHe
by Time : RUSHl
Observed Trajectories (Age)
The legend is the same as in the plot of observed trajectories. This is the same model (using TIME IN STUDY to predict the outcome), however in this graph the trajectories are re-distributed over a different metric of time: Age in years at the time of the interview.
by Age : ELSA
by Age : OCTO
by Age : RUSHc
by Age : RUSHe
by Age : RUSHl
Predicted Trajectories (Time)
The legend is the same as in the plots above. Semi-transparent blue lines represent the model prediction for each individual, computed from the estimated unique slopes and variances.
by Time : ELSA
by Time : OCTO
by Time : RUSHc
by Time : RUSHe
by Time : RUSHl
Predicted Trajectories (Age)
The legend is the same as in the plots above. Semi-transparent blue lines represent the model prediction for each individual, computed from the estimated unique slopes and variances.This is the same model as above, only the trajectories are re-distributed over a different metric of time: Age in years at the time of the interview.
by Age : ELSA
by Age : OCTO
by Age : RUSHc
by Age : RUSHe
by Age : RUSHl
\({y_{ti}} = {\beta _{0i}} + {\beta _{1i}}Tim{e_{ti}} + {\varepsilon _{ti}}\\ \\ {\beta _{0i}} = {\gamma _{0.0}} + {\gamma _{0.1}}SE{X_i} + {\gamma _{0.2}}AG{E_i} + {\gamma _{0.3}}E{D_i} + {\gamma _{0.4}}SMOKE{D_i} + {\gamma _{0.5}}CH{F_i} + {\gamma _{0.6}}M{I_i} + {\gamma _{0.7}}ST{K_i} + {\gamma _{0.7}}HP{N_i} + \\ + {\gamma _{0.8}}D{M_i} + {\gamma _{0.9}}HTND{M_i} + {\gamma _{0.11}}AGE\_SE{X_i} + {\gamma _{0.12}}AGE\_HT{N_i} + {\gamma _{0.13}}AGE\_D{M_i} + {\gamma _{0.14}}SEX\_HT{N_i} + {\gamma _{0.15}}SEX\_D{M_i} + {u_{0i}}\\ \\ {\beta _{1i}} = {\gamma _{1.0}} + {\gamma _{1.1}}SE{X_i} + {\gamma _{1.2}}AG{E_i} + {\gamma _{1.3}}E{D_i} + {\gamma _{1.4}}SMOKE{D_i} + {\gamma _{1.5}}CH{F_i} + {\gamma _{1.6}}M{I_i} + {\gamma _{1.7}}ST{K_i} + {\gamma _{1.7}}HP{N_i} + \\ + {\gamma _{1.8}}D{M_i} + {\gamma _{1.9}}HTND{M_i} + {\gamma _{1.11}}AGE\_SE{X_i} + {\gamma _{1.12}}AGE\_HT{N_i} + {\gamma _{1.13}}AGE\_D{M_i} + {\gamma _{1.14}}SEX\_HT{N_i} + {\gamma _{1.15}}SEX\_D{M_i} + {u_{1i}}\\\)